Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Zero To Mastery

AI Engineering: Build, Train and Deploy Models with AWS SageMaker

via Zero To Mastery

Go to class Write review

Overview

Launch your career in AI with this course that teaches you to build, train, and deploy your own AI models using two of the most important AI tools used in the real world: AWS SageMaker and Hugging Face. No machine learning knowledge required!
  • Build and deploy cutting-edge artificial intelligence & machine learning models to the cloud
  • Utilize powerful pre-trained models from Hugging Face with AWS SageMaker
  • Uncover the mathematical secrets behind how Large Language Models work with a deep-dive into the Transformer architecture, tokenization, and more
  • Customize models to meet the needs of your AI applications using PyTorch to create unique solutions
  • Train and test models, ensuring they deliver accurate results every time
  • Learn best practices for monitoring and optimizing your models, including load testing and scaling for massive user demand

Syllabus

  •   Section 0: Introduction
    • AI Engineering Bootcamp: Learn AWS SageMaker with Patrik Szepesi
    • Course Introduction
    • Exercise: Meet Your Classmates and Instructor
    • Course Resources
    • ZTM Plugin + Understanding Your Video Player
    • Set Your Learning Streak Goal
  •   Section 1: Introduction to AWS, Environment Setup, and Best Practices
    • Setting Up Our AWS Account
    • Set Up IAM Roles + Best Practices
    • AWS Security Best Practices
    • Set Up AWS SageMaker Domain
    • UI Domain Change
    • Setting Up SageMaker Environment
    • SageMaker Studio and Pricing
    • Let's Have Some Fun (+ More Resources)
  •   Section 2: A Gentle Introduction to HuggingFace in SageMaker
    • Setup: SageMaker Server + PyTorch
    • HuggingFace Models, Sentiment Analysis, and AutoScaling
  •   Section 3: Gathering a Dataset for Our Multiclass Text Classification Project
    • Get Dataset for Multiclass Text Classification
    • Link to the Dataset
    • Creating Our AWS S3 Bucket
    • Uploading Our Training Data to S3
    • Unlimited Updates
  •   Section 4: Exploratory Data Analysis
    • Exploratory Data Analysis - Part 1
    • Exploratory Data Analysis - Part 2
    • Data Visualization and Best Practices
  •   Section 5: Setting Up Our Training Notebook
    • Setting Up Our Training Job Notebook + Reasons to Use SageMaker
    • Python Script for HuggingFace Estimator
  •   Section 6: Introduction to Tokenizations and Encodings
    • Creating Our Optional Experiment Notebook - Part 1
    • Creating Our Optional Experiment Notebook - Part 2
    • Encoding Categorical Labels to Numeric Values
    • Understanding the Tokenization Vocabulary
    • Encoding Tokens
    • Practical Example of Tokenization and Encoding
    • Course Check-In
  •   Section 7: Setting Up Data Loading with PyTorch
    • Creating Our Dataset Loader Class
    • Setting Pytorch DataLoader
    • Implement a New Life System
  •   Section 8: Choose Your Path
    • Which Path Will You Take?
  •   Section 9: Mathematics Behind Large Language Models and Transformers
    • DistilBert vs. Bert Differences
    • Embeddings In A Continuous Vector Space
    • Introduction To Positional Encodings
    • Positional Encodings - Part 1
    • Positional Encodings - Part 2 (Even and Odd Indices)
    • Why Use Sine and Cosine Functions
    • Understanding the Nature of Sine and Cosine Functions
    • Visualizing Positional Encodings in Sine and Cosine Graphs
    • Solving the Equations to Get the Values for Positional Encodings
    • Introduction to Attention Mechanism
    • Query, Key and Value Matrix
    • Getting Started with Our Step by Step Attention Calculation
    • Calculating Key Vectors
    • Query Matrix Introduction
    • Calculating Raw Attention Scores
    • Understanding the Mathematics Behind Dot Products and Vector Alignment
    • Visualizing Raw Attention Scores in 2D
    • Converting Raw Attention Scores to Probability Distributions with Softmax
    • Normalization
    • Understanding the Value Matrix and Value Vector
    • Calculating the Final Context Aware Rich Representation for the Word "River"
    • Understanding the Output
    • Understanding Multi Head Attention
    • Multi Head Attention Example and Subsequent Layers
    • Masked Language Learning
    • Exercise: Imposter Syndrome
  •   Section 10: Customizing our Model Architecture in PyTorch
    • Getting Back to SageMaker!
    • Creating Our Custom Model Architecture with PyTorch
    • Adding the Dropout, Linear Layer, and ReLU to Our Model
  •   Section 11: Creating the Accuracy, Training, and Validation Function
    • Creating Our Accuracy Function
    • Creating Our Train Function
    • Finishing Our Train Function
    • Setting Up the Validation Function
  •   Section 12: Optimizer Functions, Model Parameters, Cross Entropy Loss Function
    • Passing Parameters In SageMaker
    • Setting Up Model Parameters For Training
    • Understanding The Mathematics Behind Cross Entropy Loss
    • Finishing Our Script.py File
  •   Section 13: Possible Resource Limit Errors Before Training and Deployment
    • Quota Increase
  •   Section 14: Starting Our Training Job and Monitoring it in AWS CloudWatch
    • Starting Our Training Job
    • Debugging Our Training Job With AWS CloudWatch
    • Analyzing Our Training Job Results
    • Link to the Finished Trained Model
  •   Section 15: Deploying our Multiclass Text Classification Endpoint in SageMaker
    • Creating Our Inference Script For Our PyTorch Model
    • Finishing Our PyTorch Inference Script
    • Setting Up Our Deployment
    • Deploying Our Model To A SageMaker Endpoint
  •   Section 16: Load Testing Our Machine Learning Model
    • Introduction to Endpoint Load Testing
    • Information about the next Lesson
    • Creating Our Test Data for Load Testing
    • Upload Testing Data to S3
    • Creating Our Model for Load Testing
    • Starting Our Load Test Job
    • Analyze Load Test Results
  •   Section 17: Production Grade Deployment of Our Machine Learning Model
    • Deploying Our Endpoint
    • Creating Lambda Function to Call Our Endpoint
    • Setting Up Our AWS API Gateway
    • Testing Our Model with Postman, API Gateway and Lambda
  •   Section 18: Cleaning Up Resources
    • Cleaning Up Resources
  •   Where To Go From Here?
    • Thank You!
    • Review This Course!
    • Become An Alumni
    • Learning Guideline
    • ZTM Events Every Month
    • LinkedIn Endorsements

Taught by

Patrik Szepesi

Reviews

Start your review of AI Engineering: Build, Train and Deploy Models with AWS SageMaker

Go to class

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free
Someone learning on their laptop while sitting on the floor.